Abstract — We propose an efficient client-based approach for channel management (channel assignment and load balancing) in 802.11-based WLANs that lead to better usage of the wireless spectrum. This approach is based on a “conflict set coloring ” formulation that jointly performs load balancing along with channel assignment. Such a formulation has a number of advantages. First, it explicitly captures interference effects at clients. Next, it intrinsically exposes opportunities for better channel re-use. Finally, algorithms based on this formulation do not depend on specific physical RF models and hence can be applied efficiently to a wide-range of in-building as well as outdoor scenarios. We have performed extensive packet-level simulations and measurements on a deployed wireless testbed of 70 APs to validate the performance of our proposed algorithms. We show that in addition to single network scenarios, the conflict set coloring formulation is well suited for channel assignment where multiple wireless networks share and contend for spectrum in the same physical space. Our results over a wide range of both simulated topologies and in-building testbed experiments indicate that our approach improves application level performance at the clients by upto three times (and atleast 50%) in comparison to current best-known techniques. I.

Abstract — Campus and enterprise wireless networks are increasingly characterized by ubiquitous coverage and rising traffic demands. Efficiently assigning channels to access points (APs) in these networks can significantly affect the performance and capacity of the WLANs. The state-of-the-art approaches assign channels statically, without considering prevailing traffic demands. In this paper, we show that the quality of a channel assignment can be improved significantly by incorporating observed traffic demands at APs and clients into the assignment process. We refer to this as traffic-aware channel assignment. We conduct extensive trace-driven and synthetic simulations and identify deployment scenarios where traffic-awareness is likely to be of great help, and scenarios where the benefit is minimal. We address key practical issues in using traffic-awareness, including measuring an interference graph, handling non-binary interference, collecting traffic demands, and predicting future demands based on historical information. We present an implementation of our assignment scheme for a 25-node WLAN testbed. Our testbed experiments show that traffic-aware assignment offers superior network performance under a wide range of real network configurations. On the whole, our approach is simple yet effective. It can be incorporated into existing WLANs with little modification to existing wireless nodes and infrastructure. I.

This thesis presents a measurement-parameterized performance study of deploy-ment factors in wireless mesh networks using four performance metrics: client cov-erage area, backhaul tier connectivity, protocol-dependent throughput, and per-user fair rates. For each metric, I identify and study deployment factors which strongly influence mesh performance via an extensive set of Monte Carlo simulations capturing realistic physical layer behavior. My findings include: (i) A random topology is un-suitable for a large-scale mesh deployment due to doubled node density requirements, yet a moderate level of perturbations from ideal grid placement has minor impact. (ii) Multiple backhaul radios per mesh node is a cost-effective deployment strategy as it leads to mesh deployments costing 50 % less than with a single-radio architecture. This work adds to the understanding of mesh deployment factors and their general impact on performance, providing further insight into practical mesh deployments. Acknowledgments First and foremost, I would like to thank my advisor, Dr. Edward Knightly, for the guidance, support, and opportunities he has provided me. He has been a

We define efficient algorithms for channel management (channel assignment and load balancing among APs) in 802.11-based WLANs that lead to better usage of the wireless spectrum. These algorithms (called CFAssign) are based on a “conflict-free set coloring ” formulation that jointly perform load balancing along with channel assignment. Such a formulation has a number of advantages. First, it explicitly captures interference effects at clients. Next, it intrinsically exposes opportunities for better channel re-use. Finally, algorithms based on this formulation do not depend on specific physical RF models and hence can be applied efficiently to a wide-range of in-building as well as outdoor scenarios. We have performed extensive packet-level simulations and measurements on a deployed wireless testbed of 70 APs to validate the performance of our proposed algorithms. We show that in addition to single network scenarios, CFAssign algorithms are well suited for channel assignment in scenarios where multiple wireless networks share the same physical space and contend for the same frequency spectrum. Our results over a wide range of scenarios indicate that CFAssign reduces the interference at clients by about 50-70 % in comparison to current best-known techniques. 1.

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This paper is the first analytical work to exhibit the substantial gains resulting from applying site specific knowledge to frequency allocation in wireless networks. Two new site-specific knowledge-based frequency allocation algorithms are shown to outperform all other published work. Site specific knowledge refers to knowledge of building layouts, the locations and electrical properties of APs, users, and physical objects. We assume that a central network controller communicates with all APs, and has site specific knowledge which enables the controller to predict, apriori, the received power from any transmitter to any receiver. Optimal frequency assignments are based on predicted powers to minimize interference and maximize throughput. Our algorithms consistently yield high throughput gains irrespective of network topology, AP activity level, and the number of APs, rogue interferers, and available channels. Our algorithms outperform the best published algorithm by up to 3.68%, 8.95%,

2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Radio Planning of Wireless Local Area Networks Sandro Bosio, Antonio Capone, Senior Member IEEE, Matteo Cesana, Member IEEE Abstract — In this paper we propose mathematical models to tackle the WLAN planning problem. Our approach aims at maximizing the network efficiency by taking into account the inter APs domain interference and the access mechanism. Both the single channel and the multiple channels WLAN planning problems are considered. We give different formulations which capture at different levels of detail the effect of interference on the network efficiency. In order to evaluate the quality of the proposed models, we obtain the optimal solutions for synthetic network instances, and propose heuristics to get suboptimal solutions in a reasonable computation time. We show that the networks planned according to our approach feature higher efficiency than the one planned using classical models, like the minimum cardinality Set Covering Problem (SCP), by privileging network solutions with low power APs installed. The achieved gain reaches 167 % in particular network scenarios. Moreover, we test the obtained solutions through simulation and real life testbed implementation; both analyses show that the networks planned with the proposed approaches are the ones with the highest saturation throughput with respect to those configurations obtained with SCP. I.

Abstract — Campus and enterprise wireless networks are increasingly characterized by ubiquitous coverage and rising traffic demands. Efficiently assigning channels to access points (APs) in these networks can significantly affect the performance and capacity of the wireless LAN. Several research studies have tackled this issue. However, even the state-of-the-art approaches assign channels without considering prevailing traffic demands. The channel assignment problem has a parallel in the wireline world, where recent work has established the tremendous effectiveness of using traffic demands in network engineering decisions. Motivated by this, our paper explores whether the quality of a channel assignment can be improved by incorporating observed traffic demands at APs and clients into the assignment process. Using extensive simulations over publicly-available wireless traffic traces, as well as synthetic settings, we show that being traffic-aware could substantially improve the overall quality of a channel assignment. We develop and evaluate practical trafficaware assignment algorithms that predict future demands based on historical information and use the predicted demands for assigning channels. Finally we demonstrate the effectiveness of traffic-aware assignment using testbed experiments. I.

Consider a large set of wireless access points owned by a company, which are scattered in a city. Each access point has a coverage region (i.e., an area it can transmit/receive data in) and a capacity (i.e., a maximum number of users it can serve). The company maintains a central coordinator, which assigns every user to one access point subject to the coverage and capacity constraints. To offer the highest quality of service (e.g., signal strength), the company wishes to minimize the average distance between users and their assigned access point. This is an instance of a well-studied problem in operations research, termed optimal assignment. Even though there exist several solutions for the static case (where user locations are fixed), there is currently no method for dynamic settings. In this paper, we consider the continuous assignment problem (CAP), where an optimal assignment must be constantly maintained between mobile users and a set of servers (e.g., access points). The fact that users are mobile necessitates real-time reassignment so that the quality of service remains high. The large scale and the time-critical nature of targeted applications require fast CAP solutions. We propose an algorithm that utilizes the geometric characteristics of the problem and significantly accelerates the initial assignment computation and its subsequent maintenance. Our method applies to different cost functions (e.g., average squared distance) and to any Minkowski distance metric (e.g., Euclidean, L1 norm, etc).

Abstract—We present new frequency allocation schemes for wireless networks and show that they outperform all other published work. Two categories of schemes are presented: 1) those purely based on measurements and 2) those that use site-specific knowledge, which refers to knowledge of building layouts, the locations and electrical properties of access points (APs), users, and physical objects. In our site-specific knowledge-based algorithms, a central network controller communicates with all APs and has site-specific knowledge so that it can aprioripredict the received power from any transmitter to any receiver. Optimal frequency assignments are based on predicted powers to minimize interference and maximize throughput. In our measurement-based algorithms, clients periodically report in situ interference measurements to their associated APs; then, the APs ’ frequency allocations are adjusted based on the reported measurements. Unlike other work, we minimize interference seen by both users and APs, useaphysical model rather than a binary model for interference, and mitigate the impact of rogue interference. Our algorithms consistently yield high throughput gains, irrespective of the network topology, AP activity level, number of APs, rogue interferers, and available channels. Our algorithms outperform the best published work by 18.5%, 97.6%, and 1180 % for median, 25th percentile, and 15th percentile user throughputs, respectively. Index Terms—Cellular networks, frequency allocation, radio spectrum management, site-specific knowledge, wireless local area network (WLAN). I.